Modern approaches for multi-model fitting [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} follow a two-step procedure. First, they generate many instances by repeatedly selecting minimal point sets and fitting model instances.
Second, a subset of the hypotheses is selected interpreting the input data points the most.
This selection is done in various ways.
For instance, a popular group of methods [2]}, [3]}, [8]}, [9]} optimizes point-to-model assignments by energy minimization using graph labeling techniques [14]}.
The energy originates from point-to-model residuals, label costs [15]}, and geometric priors [3]} such as the spatial coherence of the data points.
Another group of methods uses preference analysis based on the distribution of the residuals of data points [17]}, [4]}, [5]}, [7]}.
Also, there are techniques [6]}, [22]}, [23]} approaching the problem as hyper-graph partitioning where the instances are represented by vertices, and the points by hyper-edges.
